Editorial

2 pages, 157 KiB

Open AccessEditorial

Special Issue Editorial: Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions

by Haibo Liu

Atmosphere 2023, 14(12), 1725; https://doi.org/10.3390/atmos14121725 - 24 Nov 2023

Viewed by 644

The sixth report of the Intergovernmental Panel on Climate Change (IPCC) has confirmed that human-induced climate change is already affecting many weather and climate extremes in every region across the globe [...] Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

Research

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15 pages, 2764 KiB

Open AccessArticle

Reclassifying the Spring Maize Drought Index on the Loess Plateau under a Changing Climate

by Shujie Yuan, Nan Jiang, Jinsong Wang, Liang Xue and Lin Han

Atmosphere 2023, 14(10), 1481; https://doi.org/10.3390/atmos14101481 - 25 Sep 2023

Viewed by 795

Abstract

Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess [...] Read more.

Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess Plateau from 1997 to 2013 to determine the drought category based on the yield reduction rate. Through the drought index according to the conformity rate of category standard and individual case verification, a refined suitability drought index of spring maize on the Loess Plateau was constructed, and the spatial distribution characteristics of drought in different growth stages of spring maize were analyzed. The results showed the following: (1) The number of days in the whole growth period of spring maize in all regions of the Loess Plateau has been extended. The average sowing date of spring maize in the northwest region of the Loess Plateau was 9 April, and that in the east and central regions was 26 April. In terms of spatial distribution, each growth period was gradually delayed from west to east. (2) The correlation between relative soil humidity and yield of spring maize at the jointing stage and heading stage was the best, followed by the milky stage and mature stage, and the relative soil humidity at the sowing stage and emergence stage had little effect on the yield. (3) According to the national drought category standard “Drought Category of Spring Maize in the North”, based on the data of yield reduction rate, the drought index of spring maize on the Loess Plateau was refined by region and growth stage. The drought category index values of spring maize in different growth stages and regions changed according to the revised drought category standard, with 71.4% of the sites in the sowing seedling stage and 85.7% of the sites in the seedling jointing stage, and the revised drought category was more severe than the national drought category standard, while at 57.1% of the sites in the jointing and tasseling stages and 71.4% in the tasseling and milking stages, the revised drought category was less severe than the national drought category standard. (4) Based on the revised refined drought index for spring maize on the Loess Plateau, the spatial distribution of drought occurrence frequency across different growth stages of spring maize on the Loess Plateau was analyzed. The frequency of drought occurrence during the seeding and emergence stages was 25–75%. With the change in growth stages, the high-value area of drought occurrence frequency gradually moved northward, and the overall frequency of drought occurrence decreased. For the milky mature stage, the frequency of drought occurrence in a few regions was around 42%, and the drought frequency in most regions was between 8% and 33%. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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19 pages, 3817 KiB

Open AccessArticle

Estimating Precipitation Using LSTM-Based Raindrop Spectrum in Guizhou

by Fuzeng Wang, Yaxi Cao, Qiusong Wang, Tong Zhang and Debin Su

Atmosphere 2023, 14(6), 1031; https://doi.org/10.3390/atmos14061031 - 15 Jun 2023

Viewed by 944

Abstract

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop [...] Read more.

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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14 pages, 1941 KiB

Open AccessArticle

Assessing the Impacts of Land Use/Land Cover Changes on Water Resources of the Nile River Basin, Ethiopia

by Mohammed Gedefaw, Yan Denghua and Abel Girma

Atmosphere 2023, 14(4), 749; https://doi.org/10.3390/atmos14040749 - 21 Apr 2023

Cited by 7 | Viewed by 2687

Abstract

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall [...] Read more.

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator test and an innovative trend analysis method. Land use/land cover (LULC) change was examined using Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) with a resolution of 30 m during 2012–2022. The findings revealed that forestland and shrub land area decreased by 5.18 and 2.39%, respectively. On the other hand, area of grassland, cropland, settlements and water bodies increased by 1.56, 6.18, 0.05 and 0.11%, respectively. A significant increasing trend in precipitation was observed at the Gondar (Z = 1.69) and Motta (Z = 0.93) stations. However, the trend was decreasing at the Adet (Z = −0.32), Dangla (Z = −0.37) and Bahir Dar stations. The trend in temperature increased at all stations. The significant changes in land use/land cover may be caused by human-induced activities in the basin. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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12 pages, 6716 KiB

Open AccessArticle

Potential Predictability of Seasonal Global Precipitation Associated with ENSO and MJO

by Haibo Liu, Xiaogu Zheng, Jing Yuan and Carsten S. Frederiksen

Atmosphere 2023, 14(4), 695; https://doi.org/10.3390/atmos14040695 - 07 Apr 2023

Viewed by 1197

Abstract

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing [...] Read more.

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter component. In tropical oceans (30° E–0° W, 30° S–30° N), the consensus is that the El Nino-Southern Oscillation (ENSO, with 4–8 year cycles) is a dominant driver of the potentially predictable component, while the Madden-Julian Oscillation (MJO, with 30–90 days cycles) is a dominant driver of the unpredictable component. In this study, the consensus is verified by using the Nino3-4 SST index and a popular MJO index. It is confirmed that Nino3-4 SST does indeed explain a significant part of the potential predictable component, but only limited variability of the unpredictable component is explained by the MJO index. This raises the question of whether the MJO is dominant in the variability of the unpredictable component of the precipitation, or the current MJO indexes do not represent MJO variability well. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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18 pages, 8555 KiB

Open AccessArticle

Relationship between South China Sea Summer Monsoon and Western North Pacific Tropical Cyclones Linkages with the Interaction of Indo-Pacific Pattern

by Shengyuan Liu, Jianjun Xu, Shifei Tu, Meiying Zheng and Zhiqiang Chen

Atmosphere 2023, 14(4), 645; https://doi.org/10.3390/atmos14040645 - 29 Mar 2023

Viewed by 2213

Abstract

The South China Sea (SCS) summer monsoon (SCSSM) and Western North Pacific tropical cyclones (TCs) are both tropical systems that interact with each other on multiple scales. This study examines the differences in TCs activity characteristics between anomalous strong and weak SCSSM years, [...] Read more.

The South China Sea (SCS) summer monsoon (SCSSM) and Western North Pacific tropical cyclones (TCs) are both tropical systems that interact with each other on multiple scales. This study examines the differences in TCs activity characteristics between anomalous strong and weak SCSSM years, and explores the possible mechanisms behind these differences through the coupling relationship between tropical atmospheric circulation and oceanic surface conditions. Results show that the destructiveness of TCs over the Western North Pacific is stronger during weak SCSSM years than in strong years, whereas the opposite occurs for TCs over the SCS. The interaction between the tropical Indo-Pacific ocean and atmosphere plays a key role in the relationship between SCSSM intensity and TCs activity. In strong (weak) SCSSM years, the sea surface temperature anomaly (SSTA) in the tropical Pacific Ocean tends to correspond to a La Niña-like (El Niño-like) distribution, whereas the tropical Indian Ocean shows an Indian Ocean dipole-negative (positive) phase distribution. Moreover, Walker circulations in both the Indian and Pacific Oceans are coupled during these years, which creates a seesaw-like relationship in the conditions for TCs formation between the SCS and the Western Pacific Ocean. During weak SCSSM years, the formation and activity of TCs over the SCS are suppressed due to the weakened water vapor transport caused by abnormal easterly winds from the eastern Indian Ocean to the southern SCS. Meanwhile, the higher SSTA in the Western Pacific Ocean enhances the TCs activity. In strong SCSSM years, the enhanced monsoon drives a stronger monsoon trough, improving the convective environment over the SCS, whereas in contrast, the Western Pacific Ocean is covered by colder water, resulting in poorer conditions for TCs genesis. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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17 pages, 3843 KiB

Open AccessArticle

Trends and Variability in Flood Magnitude: A Case Study of the Floods in the Qilian Mountains, Northwest China

by Xueliang Wang, Rensheng Chen, Kailu Li, Yong Yang, Junfeng Liu, Zhangwen Liu and Chuntan Han

Atmosphere 2023, 14(3), 557; https://doi.org/10.3390/atmos14030557 - 14 Mar 2023

Cited by 3 | Viewed by 1543

Abstract

Analyzing trends in flood magnitude changes, and their underlying causes, under climate change, is a key challenge for the effective management of water resources in arid and semi-arid regions, particularly for inland rivers originating in the Qilian Mountains (QMs). Sen’s slope estimator and [...] Read more.

Analyzing trends in flood magnitude changes, and their underlying causes, under climate change, is a key challenge for the effective management of water resources in arid and semi-arid regions, particularly for inland rivers originating in the Qilian Mountains (QMs). Sen’s slope estimator and the Mann–Kendall test were used to investigate the spatial and temporal trends in flood magnitude, based on the annual maximum peak discharge (AMPD) and Peaks Over Threshold magnitude (POT3M) flood series, of twelve typical rivers, from 1970 to 2021. The results showed that, in the AMPD series, 42% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends; in the POT3M series, 25% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends. The regional differences in the QMs from east to west were that, rivers in the eastern region (e.g., Gulang, Zamu, and Xiying rivers) showed significantly decreasing trends in the AMPD and POT3M series; most rivers in the central region had non-significant trends, while the Shule river in the western region showed a significantly increasing trend. Temperatures and precipitation showed a fluctuating increasing trend after 1987, which were the main factors contributing to the change in flood magnitude trends of the AMPD and POT3M flood series in the QMs. Regional differences in precipitation, precipitation intensity, and the ratio of glacial meltwater in the eastern, central and western regions, resulted in the differences in flood magnitude trends between the east and west. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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16 pages, 4108 KiB

Open AccessArticle

Climate Change, Land Use, and Vegetation Evolution in the Upper Huai River Basin

by Abel Girma, Denghua Yan, Kun Wang, Hailu Birara, Mohammed Gedefaw, Dorjsuren Batsuren, Asaminew Abiyu, Tianlin Qin, Temesgen Mekonen and Amanuel Abate

Atmosphere 2023, 14(3), 512; https://doi.org/10.3390/atmos14030512 - 07 Mar 2023

Cited by 3 | Viewed by 1844

Abstract

Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate [...] Read more.

Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate change, land use, and vegetation evolution in the Upper Huai River Basin. The Mann–Kendall test (MK), Innovative Trend Analysis Method (ITAM), and Sen’s slope estimator test were used to detect climate change trends. The land-use/land-cover change was also examined using a transformation matrix and Normalized Difference Vegetation Index (NDVI). The results of this study revealed that precipitation has shown a slightly decreasing trend during the past 56 years. However, the air temperature has increased by 1.2 °C. The artificial and natural vegetation and wetland were decreased by 12,097 km², 3207 km², and 641 km², respectively. On the other hand, resident construction land and artificial water bodies increased by 2277 km² and 3691 km², respectively. This indicates that the land cover has significantly changed during the past 30 years. The findings of this study will have implications for predicting the water resources safety and eco-environment of The Huai River Basin. The spatial distribution showed an uneven change in the Huai River Basin. Together, we suggested that the variability of water resources availability in the Huai River Basin was mainly attributed to climate variability, while land use change plays a key role in the sub-basins, which experienced dramatic changes in land use. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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19 pages, 3815 KiB

Open AccessArticle

Assessment of Long-Term Rainfall Variability and Trends Using Observed and Satellite Data in Central Punjab, Pakistan

by Khalil Ahmad, Abhishek Banerjee, Wajid Rashid, Zilong Xia, Shahid Karim and Muhammad Asif

Atmosphere 2023, 14(1), 60; https://doi.org/10.3390/atmos14010060 - 28 Dec 2022

Cited by 10 | Viewed by 3055

Abstract

This study explores the spatio-temporal distribution and trends on monthly, seasonal, and annual scales of rainfall in the central Punjab districts of Punjab province in Pakistan by using observation and satellite data products. The daily observed data was acquired from the Pakistan Metrological [...] Read more.

This study explores the spatio-temporal distribution and trends on monthly, seasonal, and annual scales of rainfall in the central Punjab districts of Punjab province in Pakistan by using observation and satellite data products. The daily observed data was acquired from the Pakistan Metrological Department (PMD) between 1983 and 2020, along with one reanalysis, namely the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and one satellite-based daily Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks climate data record (PERSIANN-CDR) using the Google Earth Engine (GEE) web-based API platform to investigate the spatio-temporal fluctuations and inter-annual variability of rainfall in the study domain. Several statistical indices were employed to check the data similarity between observed and remotely sensed data products and applied to each district. Moreover, non-parametric techniques, i.e., Mann–Kendall (MK) and Sen’s slope estimator were applied to measure the long-term spatio-temporal trends. Remotely sensed data products reveal 422.50 mm (CHIRPS) and 571.08 mm (PERSIANN-CDR) mean annual rainfall in central Punjab. Maximum mean rainfall was witnessed during the monsoon season (70.5%), followed by pre-monsoon (15.2%) and winter (10.2%). Monthly exploration divulges that maximum mean rainfall was noticed in July (26.5%), and the minimum was in November (0.84%). The district-wise rainfall estimation shows maximum rainfall in Sialkot (931.4 mm) and minimum in Pakpattan (289.2 mm). Phase-wise analysis of annual, seasonal, and monthly trends demonstrated a sharp decreasing trend in Phase-1, averaging 3.4 mm/decade and an increasing tendency in Phase-2, averaging 9.1 mm/decade. Maximum seasonal rainfall decreased in phase-1 and increased Phase-2 during monsoon season, averaging 2.1 and 4.7 mm/decade, whereas monthly investigation showed similar phase-wise tendencies in July (1.1 mm/decade) and August (2.3 mm/decade). In addition, as district-wise analyses of annual, seasonal, and monthly trends in the last four decades reveal, the maximum declined trend was in Sialkot (18.5 mm/decade), whereas other districts witnessed an overall increasing trend throughout the years. Out of them, Gujrat district experienced the maximum increasing trend in annual terns (50.81 mm/decade), and Faisalabad (25.45 mm/decade) witnessed this during the monsoon season. The uneven variability and trends have had a crucial imprint on the local environment, mainly in the primary activities. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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12 pages, 4306 KiB

Open AccessArticle

Variation Characteristics of Rainstorms and Floods in Southwest China and Their Relationships with Atmospheric Circulation in the Summer Half-Year

by Qingxia Xie, Xiaoping Gu, Gang Li, Tianran Tang and Zhiyu Li

Atmosphere 2022, 13(12), 2103; https://doi.org/10.3390/atmos13122103 - 15 Dec 2022

Cited by 4 | Viewed by 1158

Abstract

Local climates are responding to global warming differently, and the changes in rainstorms in mountainous areas of Southwest China are of particular interest. This study, using monthly NCEP/NCAR reanalysis and daily precipitation observation of 90 meteorological stations from 1961 to 2021, analyzed the [...] Read more.

Local climates are responding to global warming differently, and the changes in rainstorms in mountainous areas of Southwest China are of particular interest. This study, using monthly NCEP/NCAR reanalysis and daily precipitation observation of 90 meteorological stations from 1961 to 2021, analyzed the temporal and spatial variation characteristics of rainstorms and floods in Southwest China and their relationship with atmospheric circulations. The results led us to the following five conclusions: (1) Rainstorms and floods in southwest China mainly occur from June to August, during which time July has the most weather events, followed by August. (2) The southwest of Guizhou province, the southern edge of Yunnan province, and regions from the east of the Sichuan Basin to the north of Guizhou have experienced more rainstorms and floods, while the northwest regions of Southwest China have had fewer. (3) Over the last 61 years, rainstorms and floods have exhibited an overall rising trend, especially in the last 10 years. The year 2012 was an abrupt inflection point in rainstorms and floods in Southwest China, from low to high frequency, while the correlation coefficient between rainstorms and floods and the global surface temperature is above the 95% significance level. (4) Rainstorms and floods exhibit changes at periods of 8 years, 16 years, and 31 years. (5) Rainstorms and floods show a good correlation with multiple variables, such as South Asian high-pressure systems west of 90°E, the upper trough front, the northwest side of the western Pacific subtropical high, and the convergence of warm and wet air in the middle and lower layers with cold air on the ground. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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13 pages, 3685 KiB

Open AccessArticle

Characteristics of Rainstorm Intensity and Its Future Risk Estimation in the Upstream of Yellow River Basin

by Wanzhi Li, Ruishan Chen, Shao Sun, Di Yu, Min Wang, Caihong Liu and Menziyi Qi

Atmosphere 2022, 13(12), 2082; https://doi.org/10.3390/atmos13122082 - 10 Dec 2022

Cited by 1 | Viewed by 1136

Abstract

Under the background of climate warming, the occurrence of extreme events upstream of the Yellow River Basin has increased significantly. Extreme precipitation tends to be even more intense, and occurs more frequently. The impacts of various extreme weather and climate events in the [...] Read more.

Under the background of climate warming, the occurrence of extreme events upstream of the Yellow River Basin has increased significantly. Extreme precipitation tends to be even more intense, and occurs more frequently. The impacts of various extreme weather and climate events in the basin have become increasingly complex, which is increasingly difficult to cope with and affects the basin’s long-term stability and ecological security. Based on the daily precipitation data of 33 meteorological stations in the upper reaches of the Yellow River Basin from 1961 to 2021, this paper analyzes the characteristics of rainstorm intensity. Moreover, combined with the simulation results of 10 global climate models of the Coupled Model Intercomparison Project (CMIP6) and the social and economic prediction data from SSPs, it analyzes the possible changes of rainstorm disaster risk in the upper reaches of the Yellow River Basin in the 21st century, under the three emission scenarios of SSP126, SSP245, and SSP370. The results show that the precipitation in the upstream area of the Yellow River Basin is increasing at a rate of 8.1 mm per 10 years, and the number of rainstorm processes and their indicators is increasing, which indicates an increase in the extremeness of precipitation; the rainstorm process intensity index shows an increasing trend, especially in the northeast region with a concentrated population and economy, where the rainstorm process intensity index is high; it is estimated that the number of rainstorm days in low-, medium-, and high-risk scenarios will increase, which leads to an increase in the social risk by at least 60% by around 2050 (2036–2065); with the increasing disaster risk, the population exposure to rainstorm disasters is also on the rise. If no measures are taken, the population exposure will increase to 7.316 million people per day by around 2050, increasing by more than double, especially in the northeast. This study shows that, with the increasing rainstorm disaster risk and population exposure in the upper reaches of the Yellow River Basin, relevant measures need to be taken to ensure the safety of people’s lives and property. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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33 pages, 6592 KiB

Open AccessArticle

Trend Analysis of Hydro-Climatological Factors Using a Bayesian Ensemble Algorithm with Reasoning from Dynamic and Static Variables

by Keerthana A and Archana Nair

Atmosphere 2022, 13(12), 1961; https://doi.org/10.3390/atmos13121961 - 24 Nov 2022

Cited by 3 | Viewed by 1585

Abstract

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes [...] Read more.

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes are examined at 385 sites in Kerala for the years 1996 to 2016 and for the months January, April, August, and November. An inference in terms of area under the probability curve for positive, zero, and negative trend was used to deduce the changes. Positive or negative changes were noticed at 19, 32, 26, and 18 locations, in that order. These well sites will be the subject of additional dynamic and static layer investigation. According to the study, additional similar trends were seen in SM during January and April, in TE during August, and in TE and VI during November. According to the monthly order, the matching percentages were 63.2%, 59.4%, 76.9%, and 66.7%. An innovative index named SMVITERA that uses dynamic layers has been created using the aforementioned variables. The average proportion of groundwater levels that follow index trends is greater. The findings of the study can assist agronomists, hydrologists, environmentalists, and industrialists in decision making for groundwater resources. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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18 pages, 6027 KiB

Open AccessArticle

A Comparison Study of Observed and the CMIP5 Modelled Precipitation over Iraq 1941–2005

by Salam A. Abbas, Yunqing Xuan, Ali H. Al-Rammahi and Haider F. Addab

Atmosphere 2022, 13(11), 1869; https://doi.org/10.3390/atmos13111869 - 09 Nov 2022

Cited by 2 | Viewed by 1677

Abstract

This paper presents an analysis of the annual precipitation observed by a network of 30 rain gauges in Iraq over a 65-year period (1941–2005). The simulated precipitation from 18 climate models in the CMIP5 project is investigated over the same area and time [...] Read more.

This paper presents an analysis of the annual precipitation observed by a network of 30 rain gauges in Iraq over a 65-year period (1941–2005). The simulated precipitation from 18 climate models in the CMIP5 project is investigated over the same area and time window. The Mann–Kendall test is used to assess the strength and the significance of the trends (if any) in both the simulations and the observations. Several exploratory techniques are used to identify the similarity (or disagreement) in the probability distributions that are fitted to both datasets. While the results show that large biases exist in the projected rainfall data compared with the observation, a clear agreement is also observed between the observed and modelled annual precipitation time series with respect to the direction of the trends of annual precipitation over the period. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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20 pages, 3695 KiB

Open AccessArticle

The Consistent Variations of Precipitable Water and Surface Water Vapor Pressure at Interannual and Long-Term Scales: An Examination Using Reanalysis

by Jiawei Hao and Er Lu

Atmosphere 2022, 13(9), 1350; https://doi.org/10.3390/atmos13091350 - 24 Aug 2022

Viewed by 1587

Abstract

Water vapor (WV) is a vital basis of water and energy cycles and varies with space and time. When researching the variations of moisture in the atmosphere, it is intuitive to think about the total WV of the atmosphere column, precipitable water (PW). [...] Read more.

Water vapor (WV) is a vital basis of water and energy cycles and varies with space and time. When researching the variations of moisture in the atmosphere, it is intuitive to think about the total WV of the atmosphere column, precipitable water (PW). It is an element that needs high-altitude observations. A surface quantity, surface WV pressure (SVP), has a close relationship to PW because of the internal physical linkage between them. The stability of their linkage at climatic scales is verified using monthly mean data from 1979 to 2021, while studies before mainly focused on daily and annual cycles in local areas. The consistency of their variations is checked with three reanalysis datasets from three angles, the interannual variations, the long-term trends, and the empirical orthogonal function (EOF) modes. Results show that the interannual correlation of SVP and PW can reach a level that is quite high and are significant in most areas, and the weak correlation mainly exists over low-latitude oceans. The long-term trends, as well as the first EOF modes of these two quantities, also show that their variations are consistent, with spatial correlation coefficients between the long-term trends of two variables that are generally over 0.6, but specific differences appearing in some regions including the Tropical Indian Ocean and Middle Africa. With the correspondence of PW and SVP, the variations of total column WV can be indicated by surface elements. The correspondence is also meaningful for the analysis of the co-variation in total column vapor and temperature. For example, we could research the relations between SVP and air temperature, and they can reflect the co-variance of total column vapor and near-surface air temperature, which can avoid analyzing the relation between column-integrated moisture content and surface air temperature directly. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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12 pages, 3628 KiB

Open AccessArticle

ENSO Impact on Winter Precipitation in the Southeast United States through a Synoptic Climate Approach

by Jian-Hua Qian, Brian Viner, Stephen Noble, David Werth and Cuihua Li

Atmosphere 2022, 13(8), 1159; https://doi.org/10.3390/atmos13081159 - 22 Jul 2022

Viewed by 1040

Abstract

The ENSO impact on winter precipitation in the Southeast United States was analyzed from the perspective of daily weather types (WTs). We calculated the dynamic contribution associated with the change in frequency of the WTs and the thermodynamic contribution due to changes in [...] Read more.

The ENSO impact on winter precipitation in the Southeast United States was analyzed from the perspective of daily weather types (WTs). We calculated the dynamic contribution associated with the change in frequency of the WTs and the thermodynamic contribution due to changes in the spatial patterns of the environmental fields of the WTs. Six WTs were obtained using a k-means clustering analysis of 850 hPa winds in reanalysis data from November to February of 1948–2022. All the WTs can only persist for a few days. The most frequent winter weather type is WT1 (shallow trough in Eastern U.S.), which can persist or likely transfer to WT4 (Mississippi River Valley ridge). WT1 becomes less frequent in El Niño years, while the frequency of WT4 does not change much. WTs 2–6 correspond to a loop of eastward propagating waves with troughs and ridges in the mid-latitude westerlies. Three WTs with a deep trough in the Southeast U.S., which are WT2 (east coast trough), WT3 (off east coast trough) and WT6 (plains trough), become more frequent in El Niño years. The more frequent deep troughs (WTs 2, 3 and 6) and less frequent shallow trough (WT1) result in above-normal precipitation in the coastal Southeast U.S. in the winter of El Niño years. WT5 (off coast Carolina High), with maximum precipitation extending from Mississippi Valley to the Great Lakes, becomes less frequent in El Niño years, which corresponds to the below-normal precipitation from the Great Lakes to Upper Mississippi and Ohio River Valley in El Niño years, and vice versa in La Niña years. The relative contribution of the thermodynamic and dynamic contribution is location dependent. On the east coast, the two contributions are similar in magnitude. Full article

(This article belongs to the Special Issue Hydroclimate in a Changing World: Recent Trends, Current Progress and Future Directions)

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